Improving recommendations in tag-based systems with spectral clustering of tag neighbors

Pan, R; Xu, G; Dolog, P

Improving recommendations in tag-based systems with spectral clustering of tag neighbors

Pan, R Xu, G

Dolog, P

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Electrical Engineering, 2012, 114 LNEE pp. 355 - 364
Issue Date:: 2012-01-01

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Field	Value	Language
dc.contributor.author	Pan, R	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Dolog, P	en_US
dc.date.issued	2012-01-01	en_US
dc.identifier.citation	Lecture Notes in Electrical Engineering, 2012, 114 LNEE pp. 355 - 364	en_US
dc.identifier.isbn	9789400727915	en_US
dc.identifier.issn	1876-1100	en_US
dc.identifier.uri	http://hdl.handle.net/10453/106626
dc.description.abstract	Tag as a useful metadata reflects the collaborative and conceptual features of documents in social collaborative annotation systems. In this paper, we propose a collaborative approach for expanding tag neighbors and investigate the spectral clustering algorithm to filter out noisy tag neighbors in order to get appropriate recommendation for users. The preliminary experiments have been conducted on MovieLens dataset to compare our proposed approach with the traditional collaborative filtering recommendation approach and naive tag neighbors expansion approach in terms of precision, and the result demonstrates that our approach could considerably improve the performance of recommendations. © 2012 Springer Science+Business Media B.V.	en_US
dc.relation.ispartof	Lecture Notes in Electrical Engineering	en_US
dc.relation.isbasedon	10.1007/978-94-007-2792-2_34	en_US
dc.title	Improving recommendations in tag-based systems with spectral clustering of tag neighbors	en_US
dc.type	Conference Proceeding
utslib.citation.volume	114 LNEE	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	114 LNEE	en_US

Abstract:

Tag as a useful metadata reflects the collaborative and conceptual features of documents in social collaborative annotation systems. In this paper, we propose a collaborative approach for expanding tag neighbors and investigate the spectral clustering algorithm to filter out noisy tag neighbors in order to get appropriate recommendation for users. The preliminary experiments have been conducted on MovieLens dataset to compare our proposed approach with the traditional collaborative filtering recommendation approach and naive tag neighbors expansion approach in terms of precision, and the result demonstrates that our approach could considerably improve the performance of recommendations. © 2012 Springer Science+Business Media B.V.

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http://hdl.handle.net/10453/106626